A proposal of a supply risk classification method based on the combination of fuzzy and consensus reaching techniques

Abstract

One of the challenges of supply chain management is to properly react to supply interruptions due to disruption risks or poor performance. In this context, supply risk assessment and mitigation based on judgments of severity and occurrence is a common approach to establish risk factor mitigation priorities. This is a decision process that entails imprecise judgments by a group of decision makers, which may be better dealt with by applying computing with words combined with soft consensus techniques. Therefore, taking into account limitations of previous studies, this paper proposes a group decision making model to classify supply risk factors using the possibility distribution-based hesitant fuzzy linguistic term set as inputs to a fuzzy rule base classification system using a non-compensatory rule base. Also, the study includes computational modeling and a pilot application of the proposed decision model in a manufacturing company. The results of the pilot application were coherent with the experts´ evaluations. Using hesitant fuzzy linguistic term sets enabled the experts to express their uncertainty about judgments in a better way. Also, the fuzzy rule base classification system is better to classify risk factors than the Mamdani FIS, which is more adequate to simply order risk factors.